A hybrid evolutionary algorithm for the job shop scheduling problem

G I Zobolas (), C D Tarantilis and G Ioannou ()
Additional contact information
G I Zobolas: Athens University of Economics and Business
C D Tarantilis: Athens University of Economics and Business
G Ioannou: Athens University of Economics and Business

Journal of the Operational Research Society, 2009, vol. 60, issue 2, 221-235

Abstract: Abstract In this paper, a hybrid metaheuristic method for the job shop scheduling problem is proposed. The optimization criterion is the minimization of makespan and the solution method consists of three components: a Differential Evolution-based algorithm to generate a population of initial solutions, a Variable Neighbourhood Search method and a Genetic Algorithm to improve the population; the latter two are interconnected. Computational experiments on benchmark data sets demonstrate that the proposed hybrid metaheuristic reaches high quality solutions in short computational times using fixed parameter settings.

Keywords: Production scheduling; job shop; evolutionary algorithms; VNS (search for similar items in EconPapers)
Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://link.springer.com/10.1057/palgrave.jors.2602534 Abstract (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pal:jorsoc:v:60:y:2009:i:2:d:10.1057_palgrave.jors.2602534

Ordering information: This journal article can be ordered from
http://www.springer. ... search/journal/41274

DOI: 10.1057/palgrave.jors.2602534

Access Statistics for this article

Journal of the Operational Research Society is currently edited by Tom Archibald and Jonathan Crook

More articles in Journal of the Operational Research Society from Palgrave Macmillan, The OR Society
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().